library(tidyverse)
library(ggthemes)
library(plotly)
library(gt)

# load the estimated tfr
e_tfr = read_csv("data/tfr_estimates.csv") %>%
  as_tibble() 

# load the wpp tfr 
wpp_tfr = e_tfr %>% filter(data_source == "WPP")

# make a table of unique combos of Source + method
unique_combinations = unique(e_tfr[c("estimate_method", "data_source")])
unique_combinations = unique_combinations[order(unique_combinations$data_source,                                              unique_combinations$estimate_method), ] %>%
    rename(`Estimate Method` = estimate_method, `Data Source` = data_source)

# export as a gt table and set column background colors
unique_combinations %>%
  gt() %>%
  tab_style(style = cell_fill(color = "#1F78B4"),
            locations = cells_column_labels(columns = 1:2))

# create the scatter plot
est = ggplot(e_tfr) +
  geom_line(aes(x = estimated_year,
                y = estimated_tfr,
                color = interaction(estimate_method, data_source))) +
  labs(x = "Estimated Year",
       y = "Estimated TFR",
       title = "Plot of TFR Estimates of Palau 1950-2020",
       color = "") +
  theme_solarized_2() +
  theme(legend.position = "bottom",
        legend.box = "vertical",
        legend.margin = margin()) +
  guides(color = guide_legend(nrow = 18, byrow = TRUE))

est

# create the scatter plot
wpp = ggplot(wpp_tfr, aes(x = estimated_year,
                          y = estimated_tfr,
                          color = "black"))+
  geom_line() +
  labs(x = "Estimated Year",
       y = "Estimated TFR",
       title = "WPP TFR Estimates for Palau 1950-2020",
       color = "") +
  theme_solarized_2() +
  theme(legend.position = "none")
wpp

# set plotly boundaries
est_plotly = ggplotly(est, width = 800, height = 2000)


# Create a list of buttons
buttons = list()

# access the levels of the color scale for the correct order

unique_combinations = apply(unique_combinations, 1,
                            function(row) paste(row, collapse = "-"))

# create a button for each level in the correct order
for (i in seq_along(unique_combinations)) {
  visibility_vals = rep(FALSE, length(unique_combinations))
  visibility_vals[i] = TRUE
  buttons[[length(buttons) + 1]] = list(
    method = "restyle",
    args = list("visible", visibility_vals),
    label = unique_combinations[i]
  )
}

# add a button to show all interactions
buttons[[length(buttons) + 1]] = list(
  method = "restyle",
  args = list("visible", rep(TRUE, length(unique_combinations))),
  label = "Show All"
)

# adjust layout
est_plotly = est_plotly %>% 
  layout(
    showlegend = FALSE,
    updatemenus = list(
      list(
        type = "buttons",
        direction = "down",
        y = -0.1,
        x = 0,
        xanchor = 'left',
        yanchor = 'top',
        buttons = buttons,
        tickfont = list(size = 5),    # adjust button font size
        pad = list(r = 10, t = 10),   # adjust padding
        showactive = TRUE
      )
    ),
    margin = list(
    b = 800  # adjust bottom margin to make space for buttons
    )
  )

# display the plot
est_plotly

# drop WPP from data
calc_tfr = e_tfr %>% filter(data_source != "WPP")

# change cols in wpp
wpp_tfr = wpp_tfr %>% select(estimated_year, estimated_tfr) %>%
  rename(estimated_year = estimated_year, wpp_tfr = estimated_tfr)

# calc difference
calc_tfr = calc_tfr %>%
  left_join(wpp_tfr, by = "estimated_year") %>%
  mutate(difference = estimated_tfr - wpp_tfr)